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Insertion-Based Modeling for End-to-End Automatic Speech Recognition

https://doi.org/10.21437/interspeech.2020-1619
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Outgoing claims: the papers this work cites, builds on, or argues with.

inferredcites →https://doi.org/10.1109/asru46091.2019.9003750openalex-snapshot
A Comparative Study on Transformer vs RNN in Speech Applications
inferredcites →https://doi.org/10.1109/icassp.1992.225858openalex-snapshot
SWITCHBOARD: telephone speech corpus for research and development
inferredcites →https://doi.org/10.1109/icassp.2015.7178964openalex-snapshot
Librispeech: An ASR corpus based on public domain audio books
inferredcites →https://doi.org/10.1109/icassp.2016.7472621openalex-snapshot
Listen, attend and spell: A neural network for large vocabulary conversational speech recognition
inferredcites →https://doi.org/10.1109/icassp.2019.8682539openalex-snapshot
Self-attention Networks for Connectionist Temporal Classification in Speech Recognition
inferredcites →https://doi.org/10.1109/icassp40776.2020.9054188openalex-snapshot
A Streaming On-Device End-To-End Model Surpassing Server-Side Conventional Model Quality and Latency
inferredcites →https://doi.org/10.1109/icsda.2017.8384449openalex-snapshot
AISHELL-1: An open-source Mandarin speech corpus and a speech recognition baseline
inferredcites →https://doi.org/10.1109/jstsp.2017.2763455openalex-snapshot
Hybrid CTC/Attention Architecture for End-to-End Speech Recognition
inferredcites →https://doi.org/10.1162/tacl_a_00292openalex-snapshot
Insertion-based Decoding with Automatically Inferred Generation Order
inferredcites →https://doi.org/10.21437/interspeech.2017-233openalex-snapshot
A Comparison of Sequence-to-Sequence Models for Speech Recognition
inferredcites →https://doi.org/10.21437/interspeech.2018-1456openalex-snapshot
ESPnet: End-to-End Speech Processing Toolkit
inferredcites →https://doi.org/10.21437/interspeech.2018-1616openalex-snapshot
Improved Training of End-to-end Attention Models for Speech Recognition
inferredcites →https://doi.org/10.21437/interspeech.2019-1938openalex-snapshot
Improving Transformer-Based End-to-End Speech Recognition with Connectionist Temporal Classification and Language Model Integration
inferredcites →https://doi.org/10.21437/interspeech.2019-2680openalex-snapshot
SpecAugment: A Simple Data Augmentation Method for Automatic Speech Recognition
inferredcites →https://doi.org/10.4230/lipics.itp.2023.19openalex-snapshot
Exploiting Generative AI to Scale up Intelligent Tutoring Systems
inferredcites →https://doi.org/10.48550/arxiv.1506.07503openalex-snapshot
Attention-Based Models for Speech Recognition
inferredcites →https://doi.org/10.48550/arxiv.1512.02595openalex-snapshot
Deep Speech 2: End-to-End Speech Recognition in English and Mandarin
inferredcites →https://doi.org/10.48550/arxiv.1711.02281openalex-snapshot
Non-Autoregressive Neural Machine Translation
inferredcites →https://doi.org/10.48550/arxiv.1905.11006openalex-snapshot
Levenshtein Transformer
inferredcites →https://doi.org/10.48550/arxiv.1906.01604openalex-snapshot
KERMIT: Generative Insertion-Based Modeling for Sequences
inferredcites →https://doi.org/10.48550/arxiv.1911.04908openalex-snapshot
Listen and Fill in the Missing Letters: Non-Autoregressive Transformer for Speech Recognition
inferredcites →https://doi.org/10.63317/52t9tccogrodopenalex-snapshot
Enhancing the TED-LIUM Corpus with Selected Data for Language Modeling and More TED Talks
inferredcites →https://doi.org/10.63317/5ipmxoif7ik2openalex-snapshot
Spontaneous Speech Corpus of Japanese

What points at this work

Incoming claims: where this work appears in other papers’ reference lists and arguments.

inferred← citeshttps://doi.org/10.2139/ssrn.4611060openalex-extracted
Sim-T: Simplify the Transformer Network by Multiplexing Technique for Speech Recognition
inferred← citeshttps://doi.org/10.1007/978-3-031-41774-0_33openalex-snapshot
Difficulties Developing a Children’s Speech Recognition System for Language with Limited Training Data
inferred← citeshttps://doi.org/10.1007/s00034-023-02570-5openalex-snapshot
RAttSR: A Novel Low-Cost Reconstructed Attention-Based End-to-End Speech Recognizer
inferred← citeshttps://doi.org/10.1007/s10772-023-10034-zopenalex-snapshot
A transformer-based network for speech recognition
inferred← citeshttps://doi.org/10.1007/s10772-024-10160-2openalex-snapshot
Sub-layer feature fusion applied to transformer model for automatic speech recognition
inferred← citeshttps://doi.org/10.1007/s11042-026-21303-9openalex-snapshot
Noise-augmented transformer-based automatic speech recognizer using a novel noise distillation system
inferred← citeshttps://doi.org/10.1109/asru51503.2021.9688157openalex-snapshot
A Comparative Study on Non-Autoregressive Modelings for Speech-to-Text Generation
inferred← citeshttps://doi.org/10.1109/asru51503.2021.9688238openalex-snapshot
Boundary and Context Aware Training for CIF-Based Non-Autoregressive End-to-End ASR
inferred← citeshttps://doi.org/10.1109/asru57964.2023.10389698openalex-snapshot
LV-CTC: Non-Autoregressive ASR With CTC and Latent Variable Models
inferred← citeshttps://doi.org/10.1109/icassp39728.2021.9413429openalex-snapshot
CASS-NAT: CTC Alignment-Based Single Step Non-Autoregressive Transformer for Speech Recognition
inferred← citeshttps://doi.org/10.1109/icassp39728.2021.9414198openalex-snapshot
Improved Mask-CTC for Non-Autoregressive End-to-End ASR
inferred← citeshttps://doi.org/10.1109/icassp39728.2021.9414594openalex-snapshot
Intermediate Loss Regularization for CTC-Based Speech Recognition
inferred← citeshttps://doi.org/10.1109/icassp39728.2021.9415093openalex-snapshot
ORTHROS: non-autoregressive end-to-end speech translation With dual-decoder
inferred← citeshttps://doi.org/10.1109/icassp43922.2022.9746067openalex-snapshot
Non-Autoregressive End-To-End Automatic Speech Recognition Incorporating Downstream Natural Language Processing
inferred← citeshttps://doi.org/10.1109/icassp43922.2022.9746227openalex-snapshot
Alignment-Learning Based Single-Step Decoding for Accurate and Fast Non-Autoregressive Speech Recognition
inferred← citeshttps://doi.org/10.1109/icassp43922.2022.9746903openalex-snapshot
Non-Autoregressive Transformer with Unified Bidirectional Decoder for Automatic Speech Recognition
inferred← citeshttps://doi.org/10.1109/lsp.2022.3152128openalex-snapshot
Hybrid Autoregressive and Non-Autoregressive Transformer Models for Speech Recognition
inferred← citeshttps://doi.org/10.1109/lsp.2023.3241558openalex-snapshot
LFEformer: Local Feature Enhancement Using Sliding Window With Deformability for Automatic Speech Recognition
inferred← citeshttps://doi.org/10.1109/lsp.2024.3358754openalex-snapshot
ESAformer: Enhanced Self-Attention for Automatic Speech Recognition
inferred← citeshttps://doi.org/10.1109/taslp.2023.3328283openalex-snapshot
End-to-End Speech Recognition: A Survey
inferred← citeshttps://doi.org/10.1186/s13636-024-00375-1openalex-snapshot
SVQ-MAE: an efficient speech pre-training framework with constrained computational resources
inferred← citeshttps://doi.org/10.15587/1729-4061.2022.252801openalex-snapshot
Identifying the influence of transfer learning method in developing an end-to-end automatic speech recognition system with a low data level
inferred← citeshttps://doi.org/10.18653/v1/2021.naacl-main.149openalex-snapshot
End-to-end ASR to jointly predict transcriptions and linguistic annotations
inferred← citeshttps://doi.org/10.18653/v1/2022.findings-emnlp.402openalex-snapshot
BERT Meets CTC: New Formulation of End-to-End Speech Recognition with Pre-trained Masked Language Model
inferred← citeshttps://doi.org/10.21437/interspeech.2021-1131openalex-snapshot
Toward Streaming ASR with Non-Autoregressive Insertion-Based Model
inferred← citeshttps://doi.org/10.21437/interspeech.2021-1556openalex-snapshot
Streaming End-to-End ASR Based on Blockwise Non-Autoregressive Models
inferred← citeshttps://doi.org/10.21437/interspeech.2021-911openalex-snapshot
Relaxing the Conditional Independence Assumption of CTC-Based ASR by Conditioning on Intermediate Predictions
inferred← citeshttps://doi.org/10.21437/interspeech.2022-11284openalex-snapshot
InterAug: Augmenting Noisy Intermediate Predictions for CTC-based ASR
inferred← citeshttps://doi.org/10.23919/apsipaasc55919.2022.9980031openalex-snapshot
Non-Autoregressive Speech Recognition with Error Correction Module
inferred← citeshttps://doi.org/10.3390/app14031300openalex-snapshot
A Light-Weight Autoregressive CNN-Based Frame Level Transducer Decoder for End-to-End ASR
inferred← citeshttps://doi.org/10.3390/electronics12132980openalex-snapshot
A Survey of Non-Autoregressive Neural Machine Translation
inferred← citeshttps://doi.org/10.4218/etrij.2021-0106openalex-snapshot
Fast offline transformer‐based end‐to‐end automatic speech recognition for real‐world applications
inferred← citeshttps://doi.org/10.48550/arxiv.2010.13047openalex-snapshot
Orthros: Non-autoregressive End-to-end Speech Translation with Dual-decoder
inferred← citeshttps://doi.org/10.48550/arxiv.2010.13270openalex-snapshot
Improved Mask-CTC for Non-Autoregressive End-to-End ASR
inferred← citeshttps://doi.org/10.48550/arxiv.2010.14725openalex-snapshot
CASS-NAT: CTC Alignment-based Single Step Non-autoregressive Transformer for Speech Recognition
inferred← citeshttps://doi.org/10.48550/arxiv.2012.10128openalex-snapshot
Toward Streaming ASR with Non-Autoregressive Insertion-based Model
inferred← citeshttps://doi.org/10.48550/arxiv.2102.03216openalex-snapshot
Intermediate Loss Regularization for CTC-based Speech Recognition
inferred← citeshttps://doi.org/10.55452/1998-6688-2025-22-4-23-30openalex-snapshot
APPLICATION OF NON-AUTOREGRESSIVE DECODING FOR KAZAKH SPEECH RECOGNITION
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